Start With The Problem, Not The Technology: Defining The User Needs Before Exploring AI Solutions

It’s become too easy to fall into the trap of starting with the technology first. The headlines make AI seem like a panacea – just inject it into any product and voila, magic! But while the hype leads many teams astray, the savvy ones know better. The right approach? Start with the problem, not the technology.

In this post, I’ll explain why fixating on AI before identifying real user needs leads projects astray, and share a better approach centered on the problem first.

The Allure of AI Hype

AI has captured the tech world’s imagination unlike any other technology.frm But behind the hype lies substance. Advancements in deep learning, neural networks, and natural language processing have unlocked capabilities previously imaginable only in science fiction.

So when teams hear about these amazing techniques, it’s tempting to retrofit an AI solution in search of a problem. If we have all these cool tools, we should use them, right?

Unfortunately, this technologist-first mindset often ends badly:

  • Teams build sophisticated algorithms that impress engineers but deliver little user value.
  • Product priorities drift based on what AI can do easily today, rather than on customer needs.
  • Business leaders force-fit AI to get a competitive edge, even when other solutions would work better.

While AI holds great promise, technology for technology’s sake fails more often than it succeeds. A better approach? Start with the problem.

Put The Problem Before Predictive Algorithms

Great products start by deeply understanding users’ needs and pains. Who is your target user? What frustrates them? Where do current solutions fall short? What delights them?

This user-focused discovery should guide which problems you choose to prioritize solving. Only after defining the problem do you consider technology choices like AI.

Otherwise, you risk acting like the allegorical person who sees every problem as a nail because they have a hammer in their hand. Just because you have a shiny AI hammer doesn’t mean every problem is a nail.

Let’s walk through an example. Say you run an electronics e-commerce site. Visitors today have to manually hunt for items. How could AI help? Don’t start there. First, deeply understand shopper needs:

  • They often don’t know exactly what item they need and rely on browsing.
  • Search is ineffective if they don’t know the product name.
  • Categorizing millions of SKUs is challenging.
  • Too many irrelevant products slows decisions.
  • They need confidence they’re considering all options.

Armed with these user insights, we can now frame the right problem to solve: “How might we enable shoppers to efficiently discover the most relevant products?”

Only after defining the problem do we consider using AI techniques like filters and recommendation engines. And even then, we validate if AI actually helps or if a simpler solution works.

Avoid Overengineering

Another benefit of focusing on the problem space first is avoiding overengineering. When technologists lead, there’s a tendency to architect complex systems that impress other engineers but sail over users’ heads.

Oftentimes data-hungry AI techniques like deep neural networks are not the optimal solution. For many problems, straightforward rules engines, heuristics and simple ML classifiers get you 95% of what users need with much less complexity.

Let user impact, not technological elegance, guide your hand. Validate if AI provides step-function improvements over baseline solutions. Be wary of overoptimizing just because you can.

Know When AI Is (and Isn’t) The Answer

Not every problem merits an AI solution. The hype outpaces reality.

AI excels at narrow, discrete tasks like classification and prediction. It complement humans for personalization and anomaly detection.

But it falls short on challenges requiring general knowledge, reasoning and common sense. AI also lags at inferring complex concepts from little data, adapting intelligently in dynamic environments, and assessing its own trustworthiness.

Before committing to AI, carefully analyze if it applies well to the specific problem and data at hand. In many cases, AI may not move the needle over simpler analytic and rules-based approaches.

The Perils of Leading With AI

Don’t let today’s shiny AI object distract from solving real customer problems. Too many companies force-fit AI just to seem cutting edge.

But AI for the sake of AI delivers little value. When technologists lead the charge, they often over-optimize solutions to impress peers instead of delight users.

The better path is understanding your customers’ needs first, and letting that guide your solution thinking. Only then determine if AI techniques like machine learning are appropriate.

Next time you face a thorny challenge, resist the urge to start with AI capabilities. Lead with the problem, consult users, clearly define objectives, and critically evaluate if AI is the highest-impact solution path. This discipline leads to products users love.

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